Causal machine learning methods for studying the effects of environmental exposures on childhood cancer using natural experiments - Project Summary:
My goal is to build an independent research program in the development of causal inference methods for
investigating environmental causes of childhood cancer. This K01 will enable me to conduct the focused,
intensive research that will lay the groundwork for that program and to acquire the environmental, biological,
and epidemiological training needed to maximize the rigor and impact of my work.
Research: We propose to develop new causal machine learning (ML) methods that enable rigorous analysis
of environmental natural experiments (NE) for estimation of the causal effects of environmental exposures on
childhood cancer. Classical approaches to studying relationships between environmental exposures and
childhood cancer are plagued with challenges and are yielding inconsistent findings. We contend that the
recent proliferation of local environmental regulatory programs has created ample relevant NEs, which provide
a powerful alternative approach to study these relationships. However, existing methods for NE analysis are
poorly-suited for environmental health contexts. In particular, existing methods fail in the presence of rare
outcomes like childhood cancer (Aim 1), and they are not able to provide insight into the timing at which
children are most susceptible to any adverse exposure effects (Aim 2). We propose causal ML methods that
overcome these challenges and apply them to a NE to study the effects of traffic-related air toxics on childhood
leukemia. We also provide open source software implementing these methods (Aim 3).
Career Development and Training: Given my extensive prior training and experience in statistics and data
science, the primary aim of the training funded by this award will be the acquisition of subject-matter
proficiency, which will provide me with the insights needed to create more effective and impactful
environmental health methods. Specifically, I will pursue knowledge in the biology and epidemiology of
childhood cancer and in environmental health and exposure biology. The training will be achieved through a
combination of (1) hands-on collaborative research as described above; (2) intensive cross-disciplinary
mentorship, with mentors specializing in environmental health, pediatric oncology, cancer biology and
epidemiology, and statistics; (3) carefully-selected coursework in the Departments of Epidemiology,
Environmental Health, and Cell Biology at Harvard; and (4) relevant conferences, workshops, and seminars. I
will place special emphasis on establishing a network of expert collaborators in all my areas of training.
Environment: The Harvard Medical Campus is home to the top research teams worldwide in both childhood
cancer and environmental health. Due to Harvard’s position at the forefront of scientific discovery in these
fields, its unparalleled resources, its vibrant intellectual atmosphere, and its promotion of collaborative science
that integrates knowledge across disciplines, it provides an ideal environment in which to train on these topics.
